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The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.
Training Courses Recommended for the Exam Preparation
Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.
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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems
The following will be discussed here:
- Capacity planning
- Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
- Tradeoffs involving latency, throughput, transactions
- Data modeling
- Designing data processing systems
- Designing data pipelines
- Data publishing and visualization (e.g., BigQuery)
- Use of distributed systems
- Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
- Selecting the appropriate storage technologies
- Online (interactive) vs. batch predictions
Google Certified Professional Data Engineer Exam Sample Questions (Q95-Q100):
NEW QUESTION # 95
You are updating the code for a subscriber to a Pub/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss. Your subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?
- A. Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to re-deliver messages that became available after the snapshot was created.
- B. Set up the Pub/Sub emulator on your local machine. Validate the behavior of your new subscriber logic before deploying it to production.
- C. Enable dead-lettering on the Pub/Sub topic to capture messages that aren’t successfully acknowledged. If an error occurs after deployment, re-deliver any messages captured by the dead-letter queue.
- D. Use Cloud Build for your deployment. If an error occurs after deployment, use a Seek operation to locate a timestamp logged by Cloud Build at the start of the deployment.
NEW QUESTION # 96
Your company’s customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations.
What should you do?
- A. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.
- B. Add a node to the MySQL cluster and build an OLAP cube there.
- C. Use an ETL tool to load the data from MySQL into Google BigQuery.
- D. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.
NEW QUESTION # 97
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat
a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)
- A. Create a new view over events_partitioned using standard SQL
- B. Create a new partitioned table using a standard SQL query
- C. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared “events”
- D. Create a new view over events using standard SQL
- E. Create a service account for the ODBC connection to use for authentication
NEW QUESTION # 98
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?
- A. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
- B. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
- C. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
- D. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
NEW QUESTION # 99
Your company is in the process of migrating its on-premises data warehousing solutions to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply updates from multiple transactional database sources on a daily basis. With BigQuery, your company hopes to improve its handling of CDC so that changes to the source systems are available to query in BigQuery in near-real time using log- based CDC streams, while also optimizing for the performance of applying changes to the data warehouse.
Which two steps should they take to ensure that changes are available in the BigQuery reporting table with minimal latency while reducing compute overhead? (Choose two.)
- A. Insert each new CDC record and corresponding operation type in real time to the reporting table, and use a materialized view to expose only the newest version of each unique record.
- B. Periodically DELETE outdated records from the reporting table.
- C. Perform a DML INSERT, UPDATE, or DELETE to replicate each individual CDC record in real time directly on the reporting table.
- D. Periodically use a DML MERGE to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
- E. Insert each new CDC record and corresponding operation type to a staging table in real time.
NEW QUESTION # 100
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